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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.21

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        V350246760

        This report has been generated by the sagc/fastqchecks analysis pipeline.

        Report generated on 2025-02-04, 11:54 ACDT based on data in: /hpc/vnx_homes/john.salamon/core/run/V350246760/work/72/1c0a9c5357f3659b86846d04d2cd61

        Change sample names:


        General Statistics

        Showing 12/25 rows and 3/15 columns.
        Sample Name% Dups% GCSeqs (M)
        AA_Liver
        AA_Liver_S1
        AA_Liver_S1_R1
        26.9%
        46%
        286.3
        AA_Liver_S1_R2
        27.8%
        46%
        286.3
        AA_Muscle
        AA_Muscle_S4
        AA_Muscle_S4_R1
        26.7%
        43%
        281.4
        AA_Muscle_S4_R2
        25.5%
        43%
        281.4
        MF_Liver
        MF_Liver_S2
        MF_Liver_S2_R1
        51.9%
        56%
        366.1
        MF_Liver_S2_R2
        52.6%
        56%
        366.1
        MF_Muscle
        MF_Muscle_S5
        MF_Muscle_S5_R1
        29.4%
        43%
        337.9
        MF_Muscle_S5_R2
        25.0%
        43%
        337.9
        SS_Liver
        SS_Liver_S3
        SS_Liver_S3_R1
        27.5%
        48%
        231.9
        SS_Liver_S3_R2
        28.3%
        48%
        231.9
        SS_Muscle
        SS_Muscle_S6
        SS_Muscle_S6_R1
        25.6%
        45%
        265.1
        SS_Muscle_S6_R2
        26.6%
        45%
        265.1
        Undetermined

        BBTools

        BBTools is a suite of fast multithreaded bioinformatics tools designed for the analysis of DNA and RNA sequence data.

        Insert sizes

        Histogram of computed insert sizes, for paired reads (ihist). Plotted data has been cut off at 99% to prevent long tails; Complete data available in original source files.

        The insert size is the length of the sequence between the sequencing adapters, which for most common insert sizes is longer than the sum of both read pairs. In some cases, the insert size is shorter than the length of the two read pairs combined, resulting in an insert size shorter than the sum of the length of the reads pairs.

        Created with MultiQC

        Insert sizes summary table

        Histogram of computed insert sizes, for paired reads (ihist). Plotted data has been cut off at 99% to prevent long tails; Complete data available in original source files.

        The insert size is the length of the sequence between the sequencing adapters, which for most common insert sizes is longer than the sum of both read pairs. In some cases, the insert size is shorter than the length of the two read pairs combined, resulting in an insert size shorter than the sum of the length of the reads pairs.

        Showing 6/6 rows and 4/4 columns.
        Sample NameMeanMedianSTDevPercentOfPairs
        AA_Liver_S1_ihist
        165.5
        158.0
        54.4
        78.7
        AA_Muscle_S4_ihist
        166.2
        159.0
        54.0
        78.5
        MF_Liver_S2_ihist
        177.8
        175.0
        54.7
        71.8
        MF_Muscle_S5_ihist
        159.0
        151.0
        51.4
        82.5
        SS_Liver_S3_ihist
        159.2
        150.0
        53.0
        78.8
        SS_Muscle_S6_ihist
        173.2
        168.0
        54.3
        78.9

        Kraken

        Kraken is a taxonomic classification tool that uses exact k-mer matches to find the lowest common ancestor (LCA) of a given sequence.DOI: 10.1186/gb-2014-15-3-r46.

        Top taxa

        The number of reads falling into the top 5 taxa across different ranks.

        To make this plot, the percentage of each sample assigned to a given taxa is summed across all samples. The counts for these top 5 taxa are then plotted for each of the 9 different taxa ranks. The unclassified count is always shown across all taxa ranks.

        The total number of reads is approximated by dividing the number of unclassified reads by the percentage of the library that they account for. Note that this is only an approximation, and that kraken percentages don't always add to exactly 100%.

        The category "Other" shows the difference between the above total read count and the sum of the read counts in the top 5 taxa shown + unclassified. This should cover all taxa not in the top 5, +/- any rounding errors.

        Note that any taxon that does not exactly fit a taxon rank (eg. - or G2) is ignored.

        Created with MultiQC

        FastQC

        FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        All samples have sequences of a single length (150bp).

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (eg PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        12 samples had less than 1% of reads made up of overrepresented sequences

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/0 rows.
        Overrepresented sequence

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        MGIKIT

        mgikit can be used to demultiplex demultiplex FASTQ files from an MGI sequencing instrument for downstream analysis.

        Lane Statistics

        Statistics about each lane for each flowcell.

        This longer string (can be markdown) helps explain how to interpret the plot

        Showing 4/4 rows and 5/5 columns.
        Run ID - LaneMb Total YieldM Total Clusters% bases ≥ Q30Mean Quality% Perfect Index
        V350246760-L03
        145365.72
        484.55
        93.13%
        35.18
        93.10%
        V350246760-L02
        145721.92
        485.74
        93.20%
        35.20
        92.97%
        V350246760-L04
        141448.21
        471.49
        92.79%
        35.09
        92.88%
        V350246760-L01
        145309.54
        484.37
        93.01%
        35.14
        93.13%

        Clusters by lane

        Number of reads per lane.

        The number of reads with different amounts of mismatches in their indexes, up to the maximum allowed during demultiplexing.

        Created with MultiQC

        Clusters by sample

        Number of reads per sample.

        Perfect index reads are those that do not have a single mismatch. All samples are aggregated across lanes combinned. Undetermined reads are treated as a separate sample.

        Created with MultiQC

        Clusters by sample for Lane: V350246760-L01

        This plot shows the number of reads per sample for Lane: V350246760-L01

        This longer string (can be markdown) helps explain how to interpret the plot

        Created with MultiQC

        Clusters by sample for Lane: V350246760-L02

        This plot shows the number of reads per sample for Lane: V350246760-L02

        This longer string (can be markdown) helps explain how to interpret the plot

        Created with MultiQC

        Clusters by sample for Lane: V350246760-L03

        This plot shows the number of reads per sample for Lane: V350246760-L03

        This longer string (can be markdown) helps explain how to interpret the plot

        Created with MultiQC

        Clusters by sample for Lane: V350246760-L04

        This plot shows the number of reads per sample for Lane: V350246760-L04

        This longer string (can be markdown) helps explain how to interpret the plot

        Created with MultiQC

        Undetermined barcodes by lane

        Count of the top twenty five most abundant undetermined barcodes by lanes.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        FastQCfastqc0.12.1
        KRAKEN2kraken22.1.3
        MGIKITmgikit1.0.0b0
        WorkflowNextflow24.04.0-edge
        sagc/fastqchecks1.0.dev0

        sagc/fastqchecks Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.

        Methods

        Data was processed using sagc/fastqchecks v1.0dev of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.04.0-edge (Di Tommaso et al., 2017) with the following command:

        nextflow run /hpc/capacity/SAGC/workflows/SAGC-fastqchecks --demultiplex mgi --manual_samplesheet /sequencers/sequencers/mgi/MGI_SampleSheets/SampleSheet_SAGCMagpieIntern_V350246760.csv --run_dir /sequencers/backup/MGI/R2130400190024/V350246760 --run_id V350246760 --outdir V350246760_outs --mgikit_template_params=--popular-template --merge_lanes true -profile sahmri -resume

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • If available, make sure to update the text to include the Zenodo DOI of version of the pipeline used.
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        sagc/fastqchecks Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        runName
        curious_kalman
        containerEngine
        singularity
        launchDir
        /hpc/vnx_homes/john.salamon/core/run/V350246760
        workDir
        /hpc/vnx_homes/john.salamon/core/run/V350246760/work
        projectDir
        /hpc/capacity/SAGC/workflows/SAGC-fastqchecks
        userName
        john.salamon
        profile
        sahmri
        configFiles
        N/A

        Kraken

        kraken2_database
        /hpc/capacity/reference/tool_specific/kraken/Standard-8
        kraken2_shared_db_memory
        /dev/shm

        Metadata

        run_id
        V350246760

        Demultiplexing-specific options

        demultiplex
        mgi
        run_dir
        /sequencers/backup/MGI/R2130400190024/V350246760
        mgikit_template_params
        --popular-template
        merge_lanes
        true
        use_lanes
        N/A

        Input/output options

        manual_samplesheet
        /sequencers/sequencers/mgi/MGI_SampleSheets/SampleSheet_SAGCMagpieIntern_V350246760.csv
        outdir
        V350246760_outs

        Institutional config options

        config_profile_description
        South Australian Health and Medical Research Institute (SAHMRI) HPC cluster.
        config_profile_contact
        John Salamon (john.salamon@sahmri.com)
        config_profile_url
        https://sahmri.org.au

        Max job request options

        max_cpus
        32
        max_memory
        375 GB
        max_time
        14d

        Generic options

        publish_dir_mode
        link